Finally, in the field of control, the goal is to handle the vehicle at the limits and track a reference trajectory as accurately as possible: low lateral tracking errors, low heading tracking errors, and low velocity tracking errors. Another goal is to achieve high control frequencies with the available computation hardware for real-time high-speed driving. Research in this field uses an enhancement of classical control approaches to maximize the lateral and longitudinal tire forces [1]}, [2]}. A big part of the research applies Model Predictive Control (MPC) methods in some variations [3]}, [4]}. The MPC solves a finite-time optimal control problem and computes an optimal sequence of vehicle state and control inputs (steering, acceleration) based on a specific vehicle dynamics model (kinematic, linear single-track, and nonlinear single-track model). Lastly, since the autonomous race car is driving repeatably around the track for multiple laps, it is suitable for the application of Iterative Learning Control (ILC) methods. With these data-driven approaches, algorithms are displayed that learn the control gap over time and apply afterward e.g., corrective steering input, to achieve a faster lap time [5]}, [6]}.
